1. Field of the Invention
The present invention relates to the technical field of image recognition and, more particularly, to a radial basis function (RBF) neural network pattern recognition method for reducing classification errors.
2. Description of Related Art
Conventionally, in the field of data processing, a RBF neural network is used in a broad variety of applications such as fault detection, fault diagnosis, speech recognition, image recognition, etc. The RBF neural network is advantageous as compared with an error back-propagation learning algorithm. This is because parameters should be repeatedly updated and trained for increasing a precision of a computer system for pattern recognition. Also, as compared with other networks, the RBF neural network is faster to be trained. It is found that all networks may have the same drawback of incompleteness of data collection in training samples. Advantageously, the RBF neural network provides low output responses to inputs that fall into regions of the data space where there is no training sample. Moreover, the RBF neural network can distinguish different learning contributions among training samples for generating different quantities of learning which are in turn used to update and enhance features of the computer system. That is, the RBF neural network may exploit localized basis functions such that learning of each input sample affects a specialized subset of the network parameters.
In a well-known process of training the computer system by the RBF neural network, a plurality of different pattern types are stored in the computer system. Also, samples for training the computer system belong to a specific one of the pattern types. The computer system calculates an output value of each training sample in respective pattern data. In a case that a maximum output value appears in the specific pattern type associated with the training samples, it means a correct recognition of the computer system. In another case that the maximum output value appears in any of other pattern types, it means that a gradient descent optimization algorithm is required to calculate a least means square error (LMSE) between the output value of in the specific pattern type and the maximum output value thereof. Consequently, the LMSE is used to generate parameters for updating the computer system in order to increase recognition accuracy.
However, the recognition accuracy of the LMSE is low even though it is advantageous in calculating an approximation function. Further, error may be accumulated as a result of an increase of the number of erroneous classifications in a maximum-likelihood classifier because the LMSE cannot correspond to a classification error calculated by the maximum-likelihood classifier. This can greatly reduce a performance of the computer system. Thus, there is desired to provide a novel pattern recognition method to mitigate and/or obviate the aforementioned problems.